Predicting Response to Immune Checkpoint Inhibitor Therapy: Emerging Role for Artificial Intelligence?

Author(s): Parissa Alerasool, Susu Zhou, Brandon Tsao, Che-Kai Tsao

Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic landscape for patients with cancer, leading to improved clinical outcomes in numerous malignancies with historically poor prognoses. However, only a subset of patients will benefit from treatment with ICIs. Due to disease heterogeneity and lack of viable targets, development of predictive biomarkers to select which patients will benefit the most have been largely disappointing. Previously, we identified clinical factors associated with outcome in solid tumor patients treated with ICIs, including those with good performance status and family history of cancer. However, such retrospective analysis from a single institutional is limiting, and there is an urgent need for more efficient and comprehensive methodology. Fast forwarding to present day, artificial intelligence has gained greater momentum as an avenue for improving diagnosis and treatment of cancer. Here, we review recent advances and potential role of applying artificial intelligence to predict ICI treatment outcomes.

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